System for determining digital ink orientation

ABSTRACT

A system for determining the orientation of digital ink is provided having a sensing pen and a processor. The system measures the orientation of the pen during writing by the pen on a surface printed with tags. Each tag encodes data on an identity of the surface associated with a digital description of the surface and on the respective location of that tag on the surface. The digital ink is generated by associating the digital description with the data encoded by the tags sensed by the pen during said writing. The orientation of the digital ink is determined using the measured orientation of the pen.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.12,618,747 filed Nov. 15, 2009, now issued U.S. Pat. No. 7,894,671,which is a continuation of U.S. patent application Ser. No. 10/531,733filed Apr. 18, 2005, now issued U.S. Pat. No. 7,630,553 which is anational phase (371) of PCT/AU03/01342, filed on Oct. 10, 2003, all ofwhich are herein incorporated by reference.

TECHNICAL FIELD

The present invention broadly relates to pen-based computing systems andhandwriting (digital ink) recognition systems, and in particular, to amethod of estimating the orientation of a segment of digital inkgenerated using a pen-based computing system, and to a pen-basedcomputing system for estimating the orientation of a segment of digitalink. The estimated orientation of the segment of digital ink can then besubsequently used in a digital ink line orientation normalizationtechnique.

CROSS REFERENCES

Various methods, systems and apparatus relating to the present inventionare disclosed in the following co-pending applications filed by theapplicant or assignee of the present invention. The disclosures of allof these co-pending applications are incorporated herein bycross-reference.

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BACKGROUND ART

Digital ink processing systems must deal with the huge variability inhandwriting and drawing that occurs due to the differing styles ofindividual writers. As a result, most systems perform a number ofpre-processing steps to limit this variation. Examples of such systemsinclude handwriting recognition systems, digital signature verificationsystems, document analysis systems, and digital ink searching systems.

An instance of such a procedure is orientation normalization which isused to reduce the variance of the input by aligning the digital ink asif it was written using a standard orientation on the page (for example,written left-to-right on a horizontal line for Latin character basedscripts). By aligning the digital ink in such away, the ink processingsystem can ignore the effects of variation in orientation, and as suchcan be made simpler, more robust, and more accurate.

Orientation normalization is usually performed as one of the first stepsin a digital ink processing system, and is used to minimize error inlater stages (for example, line, word, and character segmentation,feature extraction, etc.) Generally, the angle of a segment of digitalink relative to a standard reference angle (e.g. horizontal) isestimated and used to re-orient the digital ink such that the angle ofdigital ink matches the reference angle.

Orientation normalization for Latin character scripts is often performedusing baseline correction; where the baseline of a line of text isdefined as the imaginary natural line on which a user places charactersthat do not have descenders (e.g. “a”, “b”, “c”, “d”, “e”, “f”, “h”,etc.). This is done by estimating the baseline of a segment of digitalink and then rotating the ink to be horizontal. Whilst most systemsassume baselines are roughly linear, some systems attempt to modelbaseline drift using more sophisticated models such as splines [A.Hennig, N. Sherkat, and R. Whitrow, “Zone Estimation for Multiple Linesof Handwriting using Approximating Spline Functions”, FifthInternational. Workshop on Frontiers in Handwriting Recognition (IWFHR),pp. 325-328, September 1996].

A significant amount of research has been performed on orientationestimation and normalization for digital ink, with particular emphasison techniques that are applicable to Optical Character Recognitionsystems. Early research systems relied on heuristics and empiricalthresholds [W. Guerfali and R. Plamondon, “Normalization and restoringon-line handwriting”, Pattern Recognition, 26 (3), pp. 419-431, 1993; S.Madhvanath and V. Govindaraju, “Using holistic features in handwrittenword recognition”, United States Postal Services (USPS), pp. 183-198,1992], along with simple techniques such as linear regression throughstroke minima [R. Bozinocic and S. Srihari, “Off-line cursive scriptword recognition”; IEEE Transactions of Pattern Analysis and MachineIntelligence 11, pp. 69-83, 1989]. Due to the brittle nature of thesetechniques, more sophisticated systems using projection profiles [A.Vinciarelli and J. Luettin, “A New Normalization Technique for CursiveHandwritten Words”, Pattern Recognition Letters 22, pp. 1043-1050, 2001;M. Brown and S. Ganapathy, “Preprocessing Techniques for Cursive ScriptWord' Recognition”, Pattern Recognition 16 (5), pp. 447-458, 1983] andgeneralized projections [G. Nicchiotti and C. Scagliola, “GeneralisedProjections: a Tool for Cursive Handwriting Normalisation”, FifthInternational Conference on Document Analysis and Recognition (ICDAR),September 1999] were developed. Other techniques have since beendeveloped, including: least squares and weighted least-squares [M.Morita, S. Games, J. Facon, F. Bortolozzi, and R. Sabourin,“Mathematical Morphology and Weighted Least Squares to CorrectHandwriting Baseline Skew”, Fifth International Conference on DocumentAnalysis and Recognition (ICDAR), pp. 430-433, September 1999; T.Breuel, “Robust least square baseline fording using a branch and boundalgorithm”, Proceedings of the SPIE, pp. 20-27, 2002], geometricmodelling and pseudo-convex hull [M. Morita, F. Bortolozzi, J. Facon,and R. Sabourin, “Morphological approach of handwritten word skewcorrection”, SIBGRAPI'98, International Symposium on Computer Graphics,Image Processing and Vision, Rio de Janeiro, Brazil, pp. 456-461,October 1998], techniques based on the Hough transform [A. Rosenthal, J.Hu and M. Brown, “Size and orientation normalization of on-linehandwriting using Hough transform”, ICASSP'97, Munich, Germany, April1997], model based methods [Y. Bengio and Y. LeCun, “Word normalizationfor on-line handwritten word recognition”, Proceedings of theInternational Conference on Pattern Recognition, pp. 409-413, October1994], skew detection using Principal Component Analysis [Steinherz, N.,Intrator, and E. Rivlin. “Skew detection via principal componentsanalysis”, Proceedings of the International Conference on DocumentAnalysis and Recognition (ICDAR), pp. 153-156, 1999], and baselineestimation using approximating spline functions [A. Hennig, N. Sherkat,and R. Whitrow, “Zone Estimation for Multiple Lines of Handwriting usingApproximating Spline Functions”, Fifth International. Workshop onFrontiers in Handwriting Recognition (IWFHR), pp. 325-328, September1996].

Some orientation normalization techniques have been disclosed in priorart patent specifications, including the use of boundary projectionscombined with the Hough transform [T. Syeda-Mahmood, “Method of groupinghandwritten word segments in handwritten document images”, U.S. Pat. No.6,108,444]; a system for digit normalization of scanned images thatworks by finding the bounds of a parallelogram that completely enclosesthe character image [R. Vogt, “Handwritten digit normalization method”,U.S. Pat. No. 5,325,447; 3]; methods that use linear projection and aclustering algorithm to detect elements in a histogram that correspondto ascender, descender, and base lines [W. Bruce, et al, “Estimation ofbaseline, line spacing and character height: for handwritingrecognition”, U.S. Pat. No. 5,396,566; J. Kim, “Baseline DriftCorrection of Handwritten Text”, IBM Technical Disclosure Bulletin 25(10), Mar. 1983]; and a least squares calculation combined with rotationaround a centroid for the normalization of signatures [F. Sinden and G.Wilfong, “Method of normalizing handwritten symbols”, U.S. Pat. No.5,537,489] in an online signature verification system.

Whilst the techniques described above are sometimes effective, theysuffer from a number of significant limitations. For example, manyassume that all lines of written text are oriented at the same angle onthe page, and thus cannot handle pages of arbitrarily rotated textlines. Other limitations include the fact that the algorithms requiresignificant processing resources (e.g. Hough transform), are quantized(e.g. Hough transform), do not work well for short segments of text(e.g. projection methods), are brittle due to empirically estimatedthresholds (heuristic and rule-based techniques), or are sensitive toascenders, descenders and outliers (e.g. least squares regression andprojection techniques).

The azimuth of a writing implement is defined in [R. Poyner, “WintabInterface Specification 1.1: 16- and 32-bit API Reference”LCS/Telegraphics] as the “clockwise rotation of the cursor about the zaxis through a full circular range”. In other words, if x and y definethe horizontal and vertical axes of a sheet of paper, and z defines theaxis that is normal to the paper, the azimuth is the rotation of the penabout the z axis. Some pen-based computing systems are able to measurethe azimuth of a writing implement during the generation of digital ink,including Wacom graphics tablets and Netpage pens [K. Silverbrook et al,“Sensing Device”, WO 02/42989].

DISCLOSURE OF INVENTION

In the preferred embodiment, the invention is configured to work withthe Netpage networked computer system, a detailed description of whichis given in our co-pending applications, including in particular PCTapplication WO0242989 entitled “Sensing Device” filed 30 May 2002, PCTapplication WO0242894 entitled “Interactive Printer” filed 30 May 2002,PCT application WO0214075 “Interface Surface Printer Using InvisibleInk” filed 21 Feb. 2002, PCT application WO0242950 “Apparatus ForInteraction With A Network Computer System” filed 30 May 2002, and PCTapplication WO03034276 entitled “Digital Ink Database Searching UsingHandwriting Feature Synthesis” filed 24 Apr. 2003. It will beappreciated that not every implementation will necessarily embody all oreven most of the specific details and extensions described in theseapplications in relation to the basic system. However, the system isdescribed in its most complete form to assist in understanding thecontext in which the preferred embodiments and aspects of the presentinvention operate.

In brief summary, the preferred form of the Netpage system provides aninteractive paper-based interface to online information by utilizingpages of invisibly coded paper and an optically imaging pen. Each pagegenerated by the Netpage system is uniquely identified and stored on anetwork server, and all user interaction with the paper using theNetpage pen is captured, interpreted, and stored. Digital printingtechnology facilitates the on-demand printing of Netpage documents,allowing interactive applications to be developed. The Netpage printer,pen, and network infrastructure provide a paper-based alternative totraditional screen-based applications and online publishing services,and supports user-interface functionality such as hypertext navigationand form input.

Typically, a printer receives a document from a publisher or applicationprovider via a broadband connection, which is printed with an invisiblepattern of infrared tags that each encodes the location of the tag onthe page and a unique page identifier. As a user writes on the page, theimaging pen decodes these tags and converts the motion of the pen intodigital ink. The digital ink is transmitted over a wireless channel to arelay base station, and then sent to the network for processing andstorage. The system uses a stored description of the page to interpretthe digital ink, and performs the requested actions by interacting withan application.

Applications provide content to the user by publishing documents, andprocess the digital ink interactions submitted by the user. Typically,an application generates one or more interactive pages in response touser input, which are transmitted to the network to be stored, rendered,and finally printed as output to the user. The Netpage system allowssophisticated applications to be developed by providing services fordocument publishing, rendering, and delivery, authenticated transactionsand secure payments, handwriting recognition and digital ink searching,and user validation using biometric techniques such as signatureverification.

Generally, the present invention seeks to provide a method forestimating the orientation of a segment of digital ink using penorientation information. In one form, the technique involves usingtraining data to build a pen orientation model, which can be for anindividual writer, which is used to estimate the orientation ofsubsequently written digital ink.

The digital ink orientation estimation technique described herein seeksto overcome or ameliorate the limitations described in the prior art,and improve on current techniques by utilizing pen orientationinformation that has been previously unavailable or ignored by othersystems. Additionally, one form of the invention can use training datato generate a writer-dependent pen orientation model that is used duringorientation estimation.

In a broad form the present invention provides a method of estimatingthe orientation of a segment of digital ink, the method including thesteps of: measuring the azimuth of the pen at a sampling rate duringwriter generation of the segment of digital ink; and estimating theorientation of the segment of digital ink using the measured azimuth ofthe pen at sampled points.

Preferably, the estimated orientation of the segment of digital ink issubsequently used in a digital ink line orientation normalizationtechnique. In accordance with specific embodiments, a single, fixedorientation estimation is utilised for a line of digital ink, or, anorientation estimation that varies across a line of digital ink isutilised.

According to a further possible form of the invention, the orientationof the pen at sampled points is estimated by subtracting the meanazimuth of a digital ink training sample from the measured azimuth ofthe sampled points, and normalizing the estimated orientation to bewithin the range of 0° to 360°.

According to other specific embodiments, the segment of digital ink ismore than one character of digital ink. Also, the segment of digital inkmay be a line segment. In this form, line segmentation may be performedby measuring a change in azimuth value.

In yet further specific embodiments of the present invention, theorientation estimation uses a writer independent handwriting model; theorientation estimation uses a writer dependent handwriting model trainedusing sample digital ink input by the writer; or the writer dependenthandwriting model is trained using sample digital ink input by thewriter using a consistent baseline.

In a further broad form the present invention provides a pen-basedcomputing system for estimating the orientation of a segment of digitalink, the system including a pen-based computing pen to input digitalink, and a processor adapted to estimate the orientation of a segment ofdigital ink by measuring the azimuth of the pen at a sampling rateduring writer generation of the segment of digital ink, and estimatingthe orientation of the segment of digital ink using the measured azimuthof the pen at sampled points.

In still yet a further broad form the present invention provides apen-based computing system for estimating the orientation of a segmentof digital ink, the system including:

(1) a pen-based computing pen to input digital ink;

(2) a storage unit to store a handwriting model;

(3) a processor, the processor being adapted to:

-   -   (a) retrieve the handwriting model;    -   (b) receive a measurement of the azimuth of the pen at a        sampling rate during writer generation of the segment of digital        ink; and    -   (c) estimate the orientation of the segment of digital ink by        modifying the measured azimuth of the pen at sampled points        using the handwriting model.

BRIEF DESCRIPTION OF FIGURES

The present invention should become apparent from the followingdescription, which is given by way of example only, of a preferred butnon-limiting embodiment thereof, described in connection with theaccompanying figures.

FIG. 1 illustrates the average (bold), minimum, maximum, and standarddeviation (dashed) azimuth measurements for left-handed and right-handedwriters;

FIG. 2 illustrates the training and normalization procedure;

FIG. 3 illustrates measured azimuth vectors of the pen at a samplingrate during writer generation of the segment of digital ink; and

FIG. 4 illustrates the estimated orientation of the segment of digitalink shown in FIG. 3 after the handwriting model is applied.

FIG. 5 illustrates a pen-based computing system.

MODES FOR CARRYING OUT THE INVENTION

The following modes are described as applied to the description andclaims in order to provide a more precise understanding of the subjectmatter of the present invention.

Azimuth Measurements

A digitizing tablet was used to measure the azimuth of a pen during thegeneration of handwriting by five different writers. Digital ink wascollected using a Wacom Intuos graphics tablet with a sampling rate of100 Hz. The data collection application was developed using the WintabProgrammer Development Kit Version 1.26 [R. Poyner, “Wintab InterfaceSpecification 1.1: 16- and 32-bit API Reference”, LCS/Telegraphics, May9, 1996].

Table 1 details the azimuth measurements for the sample data collected,where the angles are measured clockwise with 0° representing a verticalline pointing to the top of the page. Note that the azimuth measurementsreveal that writer 2 is left-handed. Table 2 details the average,minimum, maximum, and standard deviation of the azimuth measurements forthe sample data for both left- and right-handed writers, with this dataillustrated in FIG. 1.

TABLE 1 Average, minimum and maximum azimuth measurements HandednessAverage Minimum Maximum Writer 1 Right 130° 109° 148° Writer 2 Left 294°275° 316° Writer 3 Right 149° 124° 173° Writer 4 Right 141° 123° 157°Writer 5 Right 133° 119° 159°

TABLE 2 Average, standard deviation, minimum and maximum measurementsHandedness Average Std. Deviation Minimum Maximum Right 138° 11.9° 109°173° Left 294°  8.2° 275° 316°

As can be noted from the results the azimuth of a pen during handwritingis relatively stable for a particular writer (as can be seen by thesmall standard deviation and difference in the minimum and maximumvalues).

Orientation Estimation

To estimate the orientation of digital ink using the azimuthmeasurements, a handwriting model is required to be available. Whilstthe technique works with a small number of writer-independent models(e.g. one for left-handed writers and another for right-handed writers)that do not require training, more accurate results are achieved using awriter-dependent model that is trained using sample input from thewriter. To do this, the system is trained using digital ink data thatwas written using a consistent, well-defined baseline. This data can bederived from normal input (for example, form input data that isconstrained to be written horizontally) or from a separate trainingprocedure. The training data is then used to generate a model for thewriter as shown in FIG. 2.

Alternatively, training can occur using arbitrary handwritten input(i.e. without explicit training data) by using an alternativeorientation estimation technique to truth the data from which thewriter-dependent model is generated. Since the training data does notneed to be large (a few letters can be sufficient), the technique usedto truth the data can be very expensive (processor intensive) since itis only run once on a small segment of ink. In addition to this,algorithms that fail in some situations can be used, since a singlesuccessful orientation estimation is all that is required for thetraining procedure.

Once the model has been generated, it can be used for line segmentationand orientation estimation and normalization. When performing linesegmentation, a large jump in the azimuth value (e.g. a value largerthan the expected variance as given by the writer-specific model) is anindication of the start of a new line of text with an orientationdifferent from that of the previous line. For orientation normalization,the model can be used to generate an estimate of the text orientationfor the line segment, which is then used to perform baselinenormalization.

As an example, FIG. 3 depicts the measured azimuth vectors for a streamof digital ink handwriting of “hello world”. The vectors represent theorientation of the pen during the generation of the ink, and the angleof the pen relative to the page can be seen to change smoothly duringthe writing. To estimate the orientation of the digital ink in thisexample, a simple mean azimuth model was used, where the mean of theazimuth values in a set of training data (a single horizontal line oftext written by the same writer) was calculated and stored:

$\begin{matrix}{\overset{\_}{a} = \frac{\sum\limits_{i = 1}^{n}a_{i}}{n}} & (1)\end{matrix}$where a_(i) is the azimuth measurement in degrees at sample i, and n isthe number of samples in the digital ink.

The mean value represents the normal azimuth that the writer holds thepen relative to the page when writing. To estimate the orientation ateach sample point, the mean values were subtracted from the azimuthvalues of the digital ink example shown in FIG. 3, with the resultsnormalized to ensure they are in the range 0°-360°:

$\begin{matrix}{a_{i} = \left\{ \begin{matrix}{a_{i} - \overset{\_}{a} + {360{^\circ}}} & {{a_{i} - \overset{\_}{a}} \leq {0{^\circ}}} \\{a_{i} - \overset{\_}{a}} & {otherwise}\end{matrix} \right.} & (2)\end{matrix}$where a_(i) is the azimuth measurement in degrees at sample i, and ā isthe mean azimuth value for the writer (as calculated previously).

The mean azimuth value derived from the training data, was approximately130°. This value was subtracted from each of the measured azimuth valuesshown in FIG. 3 giving the normalized orientation estimates are shown inFIG. 4. As can be seen in FIG. 4, the estimated orientation vectors givea good indication of the curved baseline orientation for the text “helloworld” at each point on the digital ink path. These orientation vectorscan then be used for digital ink line orientation normalization.

Once the ink baseline orientation has been estimated, a number ofdigital ink line orientation normalization techniques are possible. Asimple method for text written with a linear baseline is to find themean estimated orientation of the samples in the digital ink segment,and rotate the ink to counter this orientation. More sophisticatedtechniques include using a smoothed running estimate of the orientation,or fitting a curve (e.g. spline) to the estimated orientation vectorsand warping the digital ink segment to ensure the estimated baseline ishorizontal and linear.

This demonstrates a specific, but non-limiting, example method forestimating the orientation of digital ink using the azimuth of thewriting device and the use of this measurement to perform orientationnormalization line segmentation

A further particular embodiment of the present invention can be realisedusing a pen-based processing system, an example of which is shown inFIG. 5. The processing system 10 generally includes at least a processor11, a memory 12, a pen-based input device 13 and an output device 14,coupled together via a bus 15. An external interface 16 can also beprovided for coupling the processing system 10 to a storage device 17which houses a database 18. The memory 12 can be any form of memorydevice, for example, volatile or non-volatile memory, solid statestorage devices, magnetic devices, etc. The output device 14 caninclude, for example, a display device, monitor, printer, etc. Thestorage device 17 can be any form of storage means, for example,volatile or non-volatile memory, solid state storage devices, magneticdevices, etc.

In use, the processing system 10 is adapted to allow data or informationto be stored in and/or retrieved from the memory 12 and/or the database17. The processor 11 receives instructions via the input device 13 andcan display results to a user (or writer using the pen input device 13)via the output device 14. It should be appreciated that the processingsystem 10 may be any form of processing system, computer terminal,server, specialised hardware, or the like.

Thus, there has been provided in accordance with the present invention,a method and system for estimating the orientation of a segment ofdigital ink generated using a pen-based computing system.

The invention may also be said to broadly consist in the parts, elementsand features referred to or indicated herein, individually orcollectively, in any or all combinations of two or more of the parts,elements or features, and where specific integers are mentioned hereinwhich have known equivalents in the art to which the invention relates,such known equivalents are deemed to be incorporated herein as ifindividually set forth.

Although the preferred embodiment has been described in detail, itshould be understood that various changes, substitutions, andalterations can be made herein by one of ordinary skill in the artwithout departing from the scope of the present invention.

1. A system for determining the orientation of digital ink, the systemcomprising a sensing pen and a processor configured to: measure theorientation of the pen during writing by the pen on a surface printedwith tags, each tag encoding data on an identity of the surfaceassociated with a digital description of the surface and on therespective location of that tag on the surface, the digital ink beinggenerated by associating the digital description with the data encodedby the tags sensed by the pen during said writing; and determining theorientation of the digital ink using the measured orientation of thepen.
 2. A system as claimed in claim 1, wherein the processor isconfigured to use the determined orientation in a digital ink lineorientation normalization technique.
 3. A system as claimed in claim 1,wherein the processor is configured to use a single, fixed orientationdetermination for a line of digital ink.
 4. A system as claimed in claim1, wherein the processor is configured to use an orientationdetermination that varies across a line of digital ink.
 5. A system asclaimed in claim 1, wherein the processor is configured to normalize thedetermined orientation to be within the range of 0° to 360°.